We have developed a method for hyperspectral image data unmixing that requires neither pure pixels nor any prior knowledge about the data. Based on the well-established Alternating Direction Method of Multipliers, the problem is formulated as a biconvex constrained optimization with the constraints enforced by Bregman splitting. The resulting algorithm estimates the spectral and spatial structure in the image through a numerically stable iterative approach that removes the need for separate endmember and spatial abundance estimation steps. The method is illustrated on data collected by the SpecTIR imaging sensor.

The earlier algorithm was developed under Purchase Order 1010 P PA379 to EO-Stat, Inc. from UCLA under NSF, grant DMS-1118971., ().
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[17]

Wide Area Arial Reconnaissance (WAAR) Project sponsored by the Edgewood Chemical Biological Center with data made available through the Advanced Threat Detection Program run by DTRA and NSF., Data collected by the Applied Physics Laboratory with a Telops Hyper-Cam sensor in 2009., Information supplied by Alison Carr of APL., ().
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The earlier algorithm was developed under Purchase Order 1010 P PA379 to EO-Stat, Inc. from UCLA under NSF, grant DMS-1118971., ().
Google Scholar

[17]

Wide Area Arial Reconnaissance (WAAR) Project sponsored by the Edgewood Chemical Biological Center with data made available through the Advanced Threat Detection Program run by DTRA and NSF., Data collected by the Applied Physics Laboratory with a Telops Hyper-Cam sensor in 2009., Information supplied by Alison Carr of APL., ().
Google Scholar